دورية أكاديمية
أعرض في EDS

Elucidating Best Geospatial Estimation Method Applied to Environmental Sciences.

التفاصيل البيبلوغرافية
العنوان: Elucidating Best Geospatial Estimation Method Applied to Environmental Sciences.
المؤلفون: de Lourdes Berrios Cintrón M; Department of Health Sciences, Inter American University of Puerto Rico, Barranquitas Campus, Bo. Helechal Street 156, Barranquitas, Puerto Rico., Broomandi P; Department of Civil and Environmental Engineering, School of Engineering and Digital Sciences, Nazarbayev University, Kabanbay Batyr Ave. 53, Astana, 010000, Kazakhstan., Cárdenas-Escudero J; Analytical Chemistry Department, FCNET, University of Panama, University City, University Mail, Panama City, 3366, Panama.; Laser Chemistry Research Group, Department of Analytical Chemistry, Faculty of Chemistry, Complutense University of Madrid, Plaza de Ciencias 1, Madrid, 28040, Spain., Cáceres JO; Laser Chemistry Research Group, Department of Analytical Chemistry, Faculty of Chemistry, Complutense University of Madrid, Plaza de Ciencias 1, Madrid, 28040, Spain., Galán-Madruga D; National Reference Laboratory of Air Quality, National Centre for Environmental Health (CNSA), Carlos III Health Institute (ISCIII), Ctra. Majadahonda a Pozuelo, Madrid, 28222, Spain. david.galan@isciii.es.
المصدر: Bulletin of environmental contamination and toxicology [Bull Environ Contam Toxicol] 2023 Dec 08; Vol. 112 (1), pp. 6. Date of Electronic Publication: 2023 Dec 08.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: Springer Verlag Country of Publication: United States NLM ID: 0046021 Publication Model: Electronic Cited Medium: Internet ISSN: 1432-0800 (Electronic) Linking ISSN: 00074861 NLM ISO Abbreviation: Bull Environ Contam Toxicol Subsets: MEDLINE
أسماء مطبوعة: Original Publication: New York : Springer Verlag
مواضيع طبية MeSH: Environmental Science* , Air Pollution*, Environmental Monitoring/methods ; Algorithms ; Spatial Analysis
مستخلص: The aim of this study is to assess and identify the most suitable geospatial interpolation algorithm for environmental sciences. The research focuses on evaluating six different interpolation methods using annual average PM 10 concentrations as a reference dataset. The dataset includes measurements obtained from a target air quality network (scenery 1) and a sub-dataset derived from a partitive clustering technique (scenery 2). By comparing the performance of each interpolation algorithm using various indicators, the study aims to determine the most reliable method. The findings reveal that the kriging method demonstrates the highest performance within environmental sciences, with a spatial similarity of approximately 70% between the two scenery datasets. The performance indicators for the kriging method, including RMSE (root mean square error), MAE (mean absolute error), and MAPE (mean absolute percentage error), are measured at 3.2 µg/m 3 , 10.2 µg/m 3 , and 7.3%, respectively.This study addresses the existing gap in scientific knowledge regarding the comparison of geospatial interpolation techniques. The findings provide valuable insights for environmental managers and decision-makers, enabling them to implement effective control and mitigation strategies based on reliable geospatial information and data. In summary, this research evaluates and identifies the most suitable geospatial interpolation algorithm for environmental sciences, with the kriging method emerging as the most reliable option. The study's findings contribute to the advancement of knowledge in the field and offer practical implications for environmental management and planning.
(© 2023. The Author(s).)
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فهرسة مساهمة: Keywords: Air Quality; Geostatistical Estimation; Interpolation Algorithms and Environmental Sciences; PM10 Particles
تواريخ الأحداث: Date Created: 20231208 Date Completed: 20231216 Latest Revision: 20240131
رمز التحديث: 20240131
مُعرف محوري في PubMed: PMC10709237
DOI: 10.1007/s00128-023-03835-0
PMID: 38063862
قاعدة البيانات: MEDLINE
الوصف
تدمد:1432-0800
DOI:10.1007/s00128-023-03835-0